def macro_info_to_sql(): create_classify_table() a = ts.get_cpi() b = ts.get_ppi() c = ts.get_money_supply() c = c.iloc[:, [0, 1, 3, 5]] b = b.iloc[:, [0, 2]] result = pd.merge(a, b, how='left', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False) result = pd.merge(result, c, how='left', on=None, left_on=None, right_on=None, left_index=False, right_index=False, sort=False, suffixes=('_x', '_y'), copy=True, indicator=False) df_to_mysql('anack_macro_data', result)
def get_gdp_ppi_info(): df = ts.get_ppi() if df is not None: res = df.to_sql(microE_ppi, engine, if_exists='replace') msg = 'ok' if res is None else res print('获取工业品出厂价格指数: ' + msg + '\n') else: print('获取工业品出厂价格指数: ' + 'None' + '\n')
def get_ppi(): try: df = ts.get_ppi() engine = create_engine('mysql://*****:*****@127.0.0.1/stock?charset=utf8') df.to_sql('ppi', engine, if_exists='append') print "message" except Exception, e: e.message
def test(): ts.get_sz50s() ts.get_hs300s() ts.get_zz500s() ts.realtime_boxoffice() ts.get_latest_news() ts.get_notices(tk) ts.guba_sina() ts.get_cpi() ts.get_ppi() ts.get_stock_basics() ts.get_concept_classified() ts.get_money_supply() ts.get_gold_and_foreign_reserves() ts.top_list() #每日龙虎榜列表 ts.cap_tops() #个股上榜统计 ts.broker_tops() #营业部上榜统计 ts.inst_tops() # 获取机构席位追踪统计数据 ts.inst_detail()
def call_ppi(): key = 'ppi' stores = pd.HDFStore(COMMEN_FILE_PATH) if key not in stores: print('CALL TS GET PPI...') df = ts.get_ppi() stores[key] = df else: df = stores[key] stores.close() return df
def stat_all(tmp_datetime): # 存款利率 data = ts.get_deposit_rate() common.insert_db(data, "ts_deposit_rate", False, "`date`,`deposit_type`") # 贷款利率 data = ts.get_loan_rate() common.insert_db(data, "ts_loan_rate", False, "`date`,`loan_type`") # 存款准备金率 data = ts.get_rrr() common.insert_db(data, "ts_rrr", False, "`date`") # 货币供应量 data = ts.get_money_supply() common.insert_db(data, "ts_money_supply", False, "`month`") # 货币供应量(年底余额) data = ts.get_money_supply_bal() common.insert_db(data, "ts_money_supply_bal", False, "`year`") # 国内生产总值(年度) data = ts.get_gdp_year() common.insert_db(data, "ts_gdp_year", False, "`year`") # 国内生产总值(季度) data = ts.get_gdp_quarter() common.insert_db(data, "ts_get_gdp_quarter", False, "`quarter`") # 三大需求对GDP贡献 data = ts.get_gdp_for() common.insert_db(data, "ts_gdp_for", False, "`year`") # 三大产业对GDP拉动 data = ts.get_gdp_pull() common.insert_db(data, "ts_gdp_pull", False, "`year`") # 三大产业贡献率 data = ts.get_gdp_contrib() common.insert_db(data, "ts_gdp_contrib", False, "`year`") # 居民消费价格指数 data = ts.get_cpi() common.insert_db(data, "ts_cpi", False, "`month`") # 工业品出厂价格指数 data = ts.get_ppi() common.insert_db(data, "ts_ppi", False, "`month`") #############################基本面数据 http://tushare.org/fundamental.html # 股票列表 data = ts.get_stock_basics() print(data.index) common.insert_db(data, "ts_stock_basics", True, "`code`")
def add_ppi_page(canvas_para, length): """ 函数功能:工业品出厂价格指数 :param canvas_para: :return: """ c = canvas_para ppi_df = ts.get_ppi() ppi_df['month'] = ppi_df.apply(lambda x:stdMonthDate(x['month']), axis=1) ppi_df = ppi_df.sort_values(by='month',ascending=False).head(length).sort_values(by='month',ascending=True) ppiip = extract_point_from_df_date_x(df_origin=ppi_df, date_col='month', y_col='ppiip', timeAxis='month') ppi = extract_point_from_df_date_x(df_origin=ppi_df, date_col='month', y_col='ppi', timeAxis='month') qm = extract_point_from_df_date_x(df_origin=ppi_df, date_col='month', y_col='qm', timeAxis='month') rmi = extract_point_from_df_date_x(df_origin=ppi_df, date_col='month', y_col='rmi', timeAxis='month') pi = extract_point_from_df_date_x(df_origin=ppi_df, date_col='month', y_col='pi', timeAxis='month') ppi_industry_drawing = gen_lp_drawing([tuple(ppiip), tuple(ppi), tuple(qm), tuple(rmi), tuple(pi)], data_note=['工业品出厂价格指数', '生产资料价格指数', '采掘工业价格指数', '原材料工业价格指数', '加工工业价格指数'], time_axis='month') renderPDF.draw(drawing=ppi_industry_drawing, canvas=c, x=10, y=letter[1] * 0.6) cg = extract_point_from_df_date_x(df_origin=ppi_df, date_col='month', y_col='cg', timeAxis='month') food = extract_point_from_df_date_x(df_origin=ppi_df, date_col='month', y_col='food', timeAxis='month') clothing = extract_point_from_df_date_x(df_origin=ppi_df, date_col='month', y_col='clothing', timeAxis='month') roeu = extract_point_from_df_date_x(df_origin=ppi_df, date_col='month', y_col='roeu', timeAxis='month') dcg = extract_point_from_df_date_x(df_origin=ppi_df, date_col='month', y_col='dcg', timeAxis='month') ppi_life_drawing = gen_lp_drawing([tuple(cg), tuple(food), tuple(clothing), tuple(roeu), tuple(dcg)], data_note=['生活资料价格指数', '食品类价格指数', '衣着类价格指数', '一般日用品价格指数', '耐用消费品价格指数'], time_axis='month') renderPDF.draw(drawing=ppi_life_drawing, canvas=c, x=10, y=letter[1] * 0.2) c.showPage() return c
def get_macro(): Macro={} Macro['Depo']=ts.get_deposit_rate() Macro['Loan']=ts.get_loan_rate() Macro['RRR']=ts.get_rrr() Macro['MoneySupply']=ts.get_money_supply() Macro['MoneyBalance']=ts.get_money_supply_bal() Macro['GDPYOY']=ts.get_gdp_year() Macro['GDPQOQ']=ts.get_gdp_quarter() Macro['GDPFOR']=ts.get_gdp_for() Macro['GDPPULL']=ts.get_gdp_pull() Macro['GDPCON']=ts.get_gdp_contrib() Macro['CPI']=ts.get_cpi() Macro['PPI']=ts.get_ppi() Macro['SHIBO']=ts.shibor_data() return Macro
def return_ppi(self): ''' 工业品出厂价格指数 ''' df = ts.get_ppi() detail = {} for col in df.columns: lt = df[col].values.tolist() lt.reverse() for idx in xrange(0, len(lt)): try: if math.isnan(lt[idx]): lt[idx] = None except: pass detail[col] = lt self.reply(detail=detail)
def core_function(self, type): self.set_data() print(type) mongo = MongoClient("127.0.0.1", 27017) if (type == 'gdp_year'): print("gdp_year") df = fd.get_gdp_year() elif (type == 'gdp_quarter'): print("gdp_quarter") df = fd.get_gdp_quarter() elif (type == 'gdp_for'): print("gdp_for") df = fd.get_gdp_for() elif (type == 'gdp_pull'): print("gdp_pull") df = fd.get_gdp_pull() elif (type == 'get_money_supply_bal'): print("get_money_supply_bal") df = fd.get_money_supply_bal() elif (type == 'gdp_contrib'): print("gdp_contrib") df = fd.get_gdp_contrib() elif (type == 'get_cpi'): print("get_cpi") df = ts.get_cpi() elif (type == 'get_ppi'): print("get_ppi") df = ts.get_ppi() elif (type == 'get_rrr'): print("get_rrr") df = ts.get_rrr() elif (type == 'money_supply'): print("money_supply") df = ts.get_money_supply() elif (type == 'money_supply_bal'): print("money_supply_bal") df = ts.get_money_supply_bal() else: df = {} print(df) insert_string = df.to_json(orient='records') items = json.loads(insert_string) mongo.macro.gdp_year.insert(items)
def get_ppi(): """工业品出厂价格指数""" logger.info('Begin get PPI.') try: data_df = ts.get_ppi() except Exception as e: logger.exception('Error get PPI.') return None else: data_dicts = [] if data_df.empty: logger.warn('Empty get PPI.') else: data_dicts = [{'month': row[0], 'ppiip': row[1], 'ppi': row[2], 'qm': row[3], 'rmi': row[4], 'pi': row[5], 'cg': row[6], 'food': row[7], 'clothing': row[8], 'roeu': row[9], 'dcg': row[10]} for row in data_df.values] logger.info('Success get PPI.') return data_dicts
def __call__(self, conns): self.base = Base() self.financial_data = conns['financial_data'] '''存款利率''' deposit_rate = ts.get_deposit_rate() self.base.batchwri(deposit_rate, 'deposit_rate', self.financial_data) '''贷款利率''' loan_rate = ts.get_loan_rate() self.base.batchwri(loan_rate, 'loan_rate', self.financial_data) '''存款准备金率''' rrr = ts.get_rrr() self.base.batchwri(rrr, 'RatioOfDeposit', self.financial_data) '''货币供应量''' money_supply = ts.get_money_supply() self.base.batchwri(money_supply, 'money_supply', self.financial_data) '''货币供应量(年底余额)''' money_supply_bal = ts.get_money_supply_bal() self.base.batchwri(money_supply_bal, 'money_supply_bal', self.financial_data) '''国内生产总值(年度)''' gdp_year = ts.get_gdp_year() self.base.batchwri(gdp_year, 'gdp_year', self.financial_data) '''国内生产总值(季度)''' gdp_quarter = ts.get_gdp_quarter() self.base.batchwri(gdp_quarter, 'gdp_quarter', self.financial_data) '''三大需求对GDP贡献''' gdp_for = ts.get_gdp_for() self.base.batchwri(gdp_for, 'gdp_for', self.financial_data) '''三大产业对GDP拉动''' gdp_pull = ts.get_gdp_pull() self.base.batchwri(gdp_pull, 'gdp_pull', self.financial_data) '''三大产业贡献率''' gdp_contrib = ts.get_gdp_contrib() self.base.batchwri(gdp_contrib, 'gdp_contrib', self.financial_data) '''居民消费价格指数''' cpi = ts.get_cpi() self.base.batchwri(cpi, 'cpi', self.financial_data) '''工业品出场价格指数''' ppi = ts.get_ppi() self.base.batchwri(ppi, 'ppi', self.financial_data)
def getPPI(ann_dt_label): raw_data = ts.get_ppi() year_month_label = 'year_month' raw_data = raw_data.rename({'month': year_month_label}, axis=1) raw_data.loc[:, 'year'] = raw_data[year_month_label].apply( lambda x: x.split('.')[0]).astype('int') raw_data.loc[:, 'month'] = raw_data[year_month_label].apply( lambda x: x.split('.')[1]).astype('int') raw_data = raw_data.drop(year_month_label, axis=1) # ========= change datatype # raw_data = chgDataType(raw_data, ['year', 'month']) # ========== announcement date tmp_data = raw_data.loc[raw_data['month'] != 12] # announced month is 1 month later tmp_data.loc[:, 'announced_month'] = tmp_data['month'] + 1 tmp_data.loc[:, 'announced_year'] = tmp_data['year'] processed_data = tmp_data.copy() tmp_data = raw_data.loc[raw_data[ 'month'] == 12] # December, adding 1 month = January in the next year tmp_data.loc[:, 'announced_month'] = 1 tmp_data.loc[:, 'announced_year'] = tmp_data['year'] + 1 processed_data = processed_data.append(tmp_data) processed_data.loc[:, ann_dt_label] = processed_data.apply( lambda x: datetime.strftime( datetime(int(x['announced_year']), int(x['announced_month']), 15), '%Y-%m-%d'), axis=1) processed_data = processed_data.drop(['announced_year', 'announced_month'], axis=1) return processed_data
gdp_y = ts.get_gdp_year() gdp_q = ts.get_gdp_quarter() #三大需求对GDP贡献 gdp_for = ts.get_gdp_for() #三大产业对GDP拉动 gdp_pull = ts.get_gdp_pull() #三大产业贡献率 gdp_contrib = ts.get_gdp_contrib() cpi = ts.get_cpi() ppi = ts.get_ppi() df = ts.shibor_data() #取当前年份的数据 #df = ts.shibor_data(2014) #取2014年的数据 df.sort('date', ascending=False).head(10) df = ts.shibor_quote_data() #取当前年份的数据 #df = ts.shibor_quote_data(2014) #取2014年的数据 df.sort('date', ascending=False).head(10) #shibo均值 df = ts.shibor_ma_data() #取当前年份的数据 #df = ts.shibor_ma_data(2014) #取2014年的数据 df.sort('date', ascending=False).head(10) #贷款基础利率
def getPPI(self): file_name = 'ppi.csv' path = self.index + self.index_ppi + file_name data = ts.get_ppi() data.to_csv(path, encoding='utf-8') print(file_name)
money_supply=ts.get_money_supply() money_supply.to_csv('D:\\ts\\macro\\money_supply.csv', encoding='gbk') money_supply_bal=ts.get_money_supply_bal() money_supply_bal.to_csv('D:\\ts\\macro\\money_supply_bal.csv', encoding='gbk') gdp_year=ts.get_gdp_year() gdp_year.to_csv('D:\\ts\\macro\\gdp_year.csv', encoding='gbk') gdp_quater=ts.get_gdp_quarter() gdp_quater.to_csv('D:\\ts\\macro\\gdp_quater.csv', encoding='gbk') gdp_for=ts.get_gdp_for() gdp_for.to_csv('D:\\ts\\macro\\gdp_for.csv', encoding='gbk') gdp_pull=ts.get_gdp_pull() gdp_pull.to_csv('D:\\ts\\macro\\gdp_pull.csv', encoding='gbk') gdp_contrib=ts.get_gdp_contrib() gdp_contrib.to_csv('D:\\ts\\macro\\gdp_contrib.csv', encoding='gbk') cpi=ts.get_cpi() cpi.to_csv('D:\\ts\\macro\\cpi.csv', encoding='gbk') ppi=ts.get_ppi() ppi.to_csv('D:\\ts\\macro\\ppi.csv', encoding='gbk') print 'all done'
def macro_type(macros_type): if macros_type == 'deposit_rate': deposit_rate = ts.get_deposit_rate() if deposit_rate is not None: deposit_rate.to_sql('macros_deposit_rate', engine, flavor='mysql', if_exists='replace') elif macros_type == 'loan_rate': loan_rate = ts.get_loan_rate() if loan_rate is not None: loan_rate.to_sql('macros_loan_rate', engine, flavor='mysql', if_exists='replace') elif macros_type == 'rrr': rrr = ts.get_rrr() if rrr is not None: rrr.to_sql('macros_rrr', engine, flavor='mysql', if_exists='replace') elif macros_type == 'money_supply': money_supply = ts.get_money_supply() if money_supply is not None: money_supply.to_sql('macros_money_supply', engine, flavor='mysql', if_exists='replace') elif macros_type == 'money_supply_bal': money_supply_bal = ts.get_money_supply_bal() if money_supply_bal is not None: money_supply_bal.to_sql('macros_money_supply_bal', engine, flavor='mysql', if_exists='replace') elif macros_type == 'gdp_year': gdp_year = ts.get_gdp_year() if gdp_year is not None: gdp_year.to_sql('macros_gdp_year', engine, flavor='mysql', if_exists='replace') elif macros_type == 'gdp_quater': gdp_quater = ts.get_gdp_quarter() if gdp_quater is not None: gdp_quater.to_sql('macros_gdp_quater', engine, flavor='mysql', if_exists='replace') elif macros_type == 'gdp_for': gdp_for = ts.get_gdp_for() if gdp_for is not None: gdp_for.to_sql('macros_gdp_for', engine, flavor='mysql', if_exists='replace') elif macros_type == 'gdp_pull': gdp_pull = ts.get_gdp_pull() if gdp_pull is not None: gdp_pull.to_sql('macros_gdp_pull', engine, flavor='mysql', if_exists='replace') elif macros_type == 'gdp_contrib': gdp_contrib = ts.get_gdp_contrib() if gdp_contrib is not None: gdp_contrib.to_sql('macros_gdp_contrib', engine, flavor='mysql', if_exists='replace') elif macros_type == 'cpi': cpi = ts.get_cpi() if cpi is not None: cpi.to_sql('macros_cpi', engine, flavor='mysql', if_exists='replace') elif macros_type == 'ppi': ppi = ts.get_ppi() if ppi is not None: ppi.to_sql('macros_ppi', engine, flavor='mysql', if_exists='replace')
# -*- coding:UTF-8 -*- import tushare as ts import pymongo import json db = "MacroData" coll = "ppi" conn = pymongo.MongoClient('127.0.0.1', port=27017) df = ts.get_ppi() # index data columns(X columns) dicIndex = json.loads(df.to_json(orient='split')) for i, ind in enumerate(dicIndex['index']): d = dicIndex['data'][i][0] jsonstr = { '_id': d, dicIndex['columns'][0]: d, dicIndex['columns'][1]: dicIndex['data'][i][1], dicIndex['columns'][2]: dicIndex['data'][i][2], dicIndex['columns'][3]: dicIndex['data'][i][3], dicIndex['columns'][4]: dicIndex['data'][i][4], dicIndex['columns'][5]: dicIndex['data'][i][5], dicIndex['columns'][6]: dicIndex['data'][i][6], dicIndex['columns'][7]: dicIndex['data'][i][7], dicIndex['columns'][8]: dicIndex['data'][i][8], dicIndex['columns'][9]: dicIndex['data'][i][9] } try: conn[db][coll].insert(jsonstr) except:
''' ts.get_gdp_contrib() # 居民消费价格指数 ''' 返回值说明: month :统计月份 cpi :价格指数 ''' ts.get_cpi() # 工业品出厂价格指数 ''' 返回值说明: month :统计月份 ppiip :工业品出厂价格指数 ppi :生产资料价格指数 qm:采掘工业价格指数 rmi:原材料工业价格指数 pi:加工工业价格指数 cg:生活资料价格指数 food:食品类价格指数 clothing:衣着类价格指数 roeu:一般日用品价格指数 dcg:耐用消费品价格指数 ''' ts.get_ppi()
def job_5(): try: print("I'm working......宏观经济数据") # 存款利率 deposit_rate = ts.get_deposit_rate() data = pd.DataFrame(deposit_rate) data.to_sql('deposit_rate',engine,index=True,if_exists='replace') print("存款利率......done") # 贷款利率 loan_rate = ts.get_loan_rate() data = pd.DataFrame(loan_rate) data.to_sql('loan_rate',engine,index=True,if_exists='replace') print("贷款利率......done") # 存款准备金率 rrr = ts.get_rrr() data = pd.DataFrame(rrr) data.to_sql('rrr',engine,index=True,if_exists='replace') print("存款准备金率......done") # 货币供应量 money_supply = ts.get_money_supply() data = pd.DataFrame(money_supply) data.to_sql('money_supply',engine,index=True,if_exists='replace') print("货币供应量......done") # 货币供应量(年底余额) money_supply_bal = ts.get_money_supply_bal() data = pd.DataFrame(money_supply_bal) data.to_sql('money_supply_bal',engine,index=True,if_exists='replace') print("货币供应量(年底余额)......done") # 国内生产总值(年度) gdp_year = ts.get_gdp_year() data = pd.DataFrame(gdp_year) data.to_sql('gdp_year',engine,index=True,if_exists='replace') print("国内生产总值(年度)......done") # 国内生产总值(季度) gdp_quarter = ts.get_gdp_quarter() data = pd.DataFrame(gdp_quarter) data.to_sql('gdp_quarter',engine,index=True,if_exists='replace') print("国内生产总值(季度)......done") # 三大需求对GDP贡献 gdp_for = ts.get_gdp_for() data = pd.DataFrame(gdp_for) data.to_sql('gdp_for',engine,index=True,if_exists='replace') print("三大需求对GDP贡献......done") # 三大产业对GDP拉动 gdp_pull = ts.get_gdp_pull() data = pd.DataFrame(gdp_pull) data.to_sql('gdp_pull',engine,index=True,if_exists='replace') print("三大产业对GDP拉动......done") # 三大产业贡献率 gdp_contrib = ts.get_gdp_contrib() data = pd.DataFrame(gdp_contrib) data.to_sql('gdp_contrib',engine,index=True,if_exists='replace') print("三大产业贡献率......done") # 居民消费价格指数 cpi = ts.get_cpi() data = pd.DataFrame(cpi) data.to_sql('cpi',engine,index=True,if_exists='replace') print("居民消费价格指数......done") # 工业品出厂价格指数 ppi = ts.get_ppi() data = pd.DataFrame(ppi) data.to_sql('ppi',engine,index=True,if_exists='replace') print("工业品出厂价格指数......done") except Exception as e: print(e)
def getppidb(): ppi = ts.get_ppi() ppi.to_sql('ppi_data',ENGINE,if_exists = 'append')
def ppi(): return ts.get_ppi()
#g_y.sort_index(inplace=True) # g_y.sort_values(by='year',inplace=True) g_y.head() plt.plot(g_y.year,g_y.gdp) g_y1=g_y[g_y.year>=1990];g_y1 plt.plot(g_y1.year,g_y1.gdp) g_y2=g_y1[['pi','si','ti']] g_y2.index=g_y1.year; g_y2 g_y2.plot(kind='bar') g_y2.plot(kind='line') ###10.3.3 工业品出厂价格指数分析 g_p=ts.get_ppi() g_p.info() g_p g_p.sort_values(by='month',inplace=True); g_p g_p.index=g_p.month;g_p g_p.plot(); #工业品价格指数 g_p1=g_p[['ppiip','ppi','qm','rmi','pi']].dropna() g_p1.plot(); #生活价格指数 g_p2=g_p[['cg','food','clothing','roeu','dcg']].dropna();g_p2 g_p2.plot(grid=True) ###10.4 电影票房数据的实时分析 #实时票房 #获取实时电影票房数据,30分钟更新一次票房数据,可随时调用。
def init(engine, session): tbl = "macro_deposit" tsl.log(tbl + " start...") df = ts.get_deposit_rate() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_loan" tsl.log(tbl + " start...") df = ts.get_loan_rate() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_rrr" tsl.log(tbl + " start...") df = ts.get_rrr() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_money_supply" tsl.log(tbl + " start...") df = ts.get_money_supply() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_money_supply_year" tsl.log(tbl + " start...") df = ts.get_money_supply_bal() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_gdp_year" tsl.log(tbl + " start...") df = ts.get_gdp_year() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_gdp_quarter" tsl.log(tbl + " start...") df = ts.get_gdp_quarter() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_gdp_for" tsl.log(tbl + " start...") df = ts.get_gdp_for() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_gdp_pull" tsl.log(tbl + " start...") df = ts.get_gdp_pull() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_gdp_contrib" tsl.log(tbl + " start...") df = ts.get_gdp_contrib() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_cpi" tsl.log(tbl + " start...") df = ts.get_cpi() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "macro_ppi" tsl.log(tbl + " start...") df = ts.get_ppi() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done") tbl = "gold_and_foreign_reserves" tsl.log(tbl + " start...") df = ts.get_gold_and_foreign_reserves() df.to_sql(tbl,engine,if_exists='replace') tsl.log(tbl + " done")
def call_ppi(): df = ts.get_ppi() return df
def load_macro_economy(): # 下载存款利率 try: rs = ts.get_deposit_rate() pd.DataFrame.to_sql(rs, "deposit_rate", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True) print("下载存款利率ok") except: print("下载存款利率出错") # 下载贷款利率 try: rs = ts.get_loan_rate() pd.DataFrame.to_sql(rs, "loan_rate", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True) print("下载贷款利率ok") except: print("下载贷款利率出错") # 下载存款准备金率 try: rs = ts.get_rrr() pd.DataFrame.to_sql(rs, "rrr", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True) print("下载存款准备金率ok") except: print("下载存款准备金率出错") # 下载货币供应量 try: rs = ts.get_money_supply() pd.DataFrame.to_sql(rs, "money_supply", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True) print("下载货币供应量ok") except: print("下载货币供应量出错") # 下载货币供应量(年底余额) try: rs = ts.get_money_supply_bal() pd.DataFrame.to_sql( rs, "money_supply_bal", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True ) print("下载货币供应量(年底余额)ok") except: print("下载货币供应量(年底余额)出错") # 下载国内生产总值(年度) try: rs = ts.get_gdp_year() pd.DataFrame.to_sql(rs, "gdp_year", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True) print("下载国内生产总值(年度)ok") except: print("下载国内生产总值(年度)出错") # 下载国内生产总值(季度) try: rs = ts.get_gdp_quarter() pd.DataFrame.to_sql(rs, "gdp_quarter", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True) print("下载国内生产总值(季度)ok") except: print("下载国内生产总值(季度)出错") # 下载三大需求对GDP贡献 try: rs = ts.get_gdp_for() pd.DataFrame.to_sql(rs, "gdp_for", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True) print("下载三大需求对GDP贡献ok") except: print("下载三大需求对GDP贡献出错") # 下载三大产业对GDP拉动 try: rs = ts.get_gdp_pull() pd.DataFrame.to_sql(rs, "gdp_pull", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True) print("下载三大产业对GDP拉动ok") except: print("下载三大产业对GDP拉动出错") # 下载三大产业贡献率 try: rs = ts.get_gdp_contrib() pd.DataFrame.to_sql(rs, "gdp_contrib", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True) print("下载三大产业贡献率ok") except: print("下载三大产业贡献率出错") # 下载居民消费价格指数 try: rs = ts.get_cpi() pd.DataFrame.to_sql(rs, "gdp_cpi", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True) print("下载居民消费价格指数ok") except: print("下载居民消费价格指数出错") # 下载工业品出厂价格指数 try: rs = ts.get_ppi() pd.DataFrame.to_sql(rs, "gdp_ppi", con=conn_macro_economy, flavor="mysql", if_exists="replace", index=True) print("下载工业品出厂价格指数ok") except: print("下载工业品出厂价格指数出错")